二进制阈值网络作为生物网络的自然零模型
Binary threshold networks as a natural null model for biological networks
摘要：
神经网络和遗传网络的自旋模型被认为是优雅的，因为它们可以使用自旋玻璃和磁性系统的统计力学工具。然而，传统的自旋系统中的变量的选择可能会导致一些模型中的问题时，参数的选择是不现实的，从生物学的角度来看。显然，这可能会限制模型作为生物系统模板模型的作用。也许不那么明显，随机网络的集合也会受到影响，并可能表现出不同的临界特性。我们在这里考虑一个原型网络模型，在其本地机制在生物学上是合理的。我们研究了具有两个特征性质的离散动力学网络：具有二进制状态0和1的节点，以及具有$Θ_0(0)=0$的修改的阈值函数。我们探讨了这种节点的随机网络的临界性质，并找到了一个临界连通性$K_c=2.0$与活动消失在临界点。最后，我们观察到，本模型允许一个更自然的实施最近的模型，芽殖酵母和裂变酵母细胞周期控制网络。
英文摘要：
Spin models of neural networks and genetic networks are considered elegant as they are accessible to statistical mechanics tools for spin glasses and magnetic systems. However, the conventional choice of variables in spin systems may cause problems in some models when parameter choices are unrealistic from a biological perspective. Obviously, this may limit the role of a model as a template model for biological systems. Perhaps less obviously, also ensembles of random networks are affected and may exhibit different critical properties. We consider here a prototypical network model that is biologically plausible in its local mechanisms. We study a discrete dynamical network with two characteristic properties: Nodes with binary states 0 and 1, and a modified threshold function with $Θ_0(0)=0$. We explore the critical properties of random networks of such nodes and find a critical connectivity $K_c=2.0$ with activity vanishing at the critical point. Finally, we observe that the present model allows a more natural implementation of recent models of budding yeast and fission yeast cell-cycle control networks.
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